0000000000222646
AUTHOR
S. Nischwitz
Novel multiple sclerosis susceptibility loci implicated in epigenetic regulation
Genome-wide study in Germans identifies four novel multiple sclerosis risk genes and confirms already known gene loci.
Successful Replication of GWAS Hits for Multiple Sclerosis in 10,000 Germans Using the Exome Array
Genome-wide association studies (GWAS) successfully identified various chromosomal regions to be associated with multiple sclerosis (MS). The primary aim of this study was to replicate reported associations from GWAS using an exome array in a large German study. German MS cases (n = 4,476) and German controls (n = 5,714) were genotyped using the Illumina HumanExome v1-Chip. Genotype calling was performed with the Illumina Genome Studio(TM) Genotyping Module, followed by zCall. Single-nucleotide polymorphisms (SNPs) in seven regions outside the human leukocyte antigen (HLA) region showed genome-wide significant associations with MS (P values < 5 × 10(-8) ). These associations have been repor…
Longitudinal prevalence and determinants of pain in multiple sclerosis: results from the German National Multiple Sclerosis Cohort study
Pain is frequent in multiple sclerosis (MS) and includes different types, with neuropathic pain (NP) being most closely related to MS pathology. However, prevalence estimates vary largely, and causal relationships between pain and biopsychosocial factors in MS are largely unknown. Longitudinal studies might help to clarify the prevalence and determinants of pain in MS. To this end, we analyzed data from 410 patients with newly diagnosed clinically isolated syndrome or relapsing-remitting MS participating in the prospective multicenter German National MS Cohort Study (NationMS) at baseline and after 4 years. Pain was assessed by self-report using the PainDETECT Questionnaire. Neuropsychiatri…
DeepWAS: Multivariate genotype-phenotype associations by directly integrating regulatory information using deep learning
Genome-wide association studies (GWAS) identify genetic variants associated with traits or diseases. GWAS never directly link variants to regulatory mechanisms. Instead, the functional annotation of variants is typically inferred by post hoc analyses. A specific class of deep learning-based methods allows for the prediction of regulatory effects per variant on several cell type-specific chromatin features. We here describe “DeepWAS”, a new approach that integrates these regulatory effect predictions of single variants into a multivariate GWAS setting. Thereby, single variants associated with a trait or disease are directly coupled to their impact on a chromatin feature in a cell type. Up to…